Design and validation of novel maize grain cleaning loss detection system based on classification models of particle time-domain signals

Abstract

Quantitative evaluation of grain cleaning loss is a central concern in maize intelligent direct harvesting. This study integrates particle time-domain signals with machine learning models to develop a novel grain cleaning loss detection system and create modular embedded companion software. Specifically, collisional time-domain signals of grains and impurities with varying mass gradients are captured, and abnormal data are eliminated utilizing the Mahalanobis Distance (MD) algorithm. Signal classification models incorporating diverse preprocessing methods are trained using Naive Bayes (NB) and Support Vector Machine (SVM) algorithms. Model performance is evaluated through internal leave-one-out-cross-validation (LOOCV) and external validation sets. The best classification model is integrated into the grain loss detection software, and bench tests verify real-time cleaning loss rates for detection systems. Results demonstrate that the SVM model with Savitzky-Golay (S-G) smoothing + Second-Order Derivative (SOD) preprocessing achieves the highest predictive accuracy, with 97.44 % and 96.15 % in training and validation sets, respectively. When the feeding rate of maize mixtures is 5 kg/s, absolute grain loss rates for three measurements are 1.62 %, 1.72 % and 1.86 %, respectively. Compared to traditional loss detection systems with multifunctional circuits, the novel loss detection system enhances the average accuracy of grain loss rate by 6.54, 6.82 and 8.93 percentage points. Hence, the proposed method and apparatus can be applied to detect grain cleaning loss quantitatively.